Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible to train “deep” ConvNets (also popularized under the keyword: “deep learning”) for computer vision classification tasks. ConvNets features are trained from the data in a fully supervised fashion. This has major advantages over more traditional CAD approaches that use hand-crafted features, designed from human experience. This means that ConvNets have a better chance of capturing the “essence” of the imaging data set used for training than when using hand-crafted features [1]}. Great advances in classification of natural images have been achieved [2]}, [3]}. Studies that have tried to apply deep learning and ConvNets to medical imaging applications also showed promise, e.g. [4]}, [5]}, [6]}. In particular, ConvNets have been applied successfully in biomedical applications such as digital pathology [7]}. In this work, we apply ConvNets for pancreas segmentation. Our motivation is partially inspired by the spirit of hybrid systems using both parametric and non-parametric models for hierarchical coarse-to-fine classification using ConvNets [8]}. This hierarchical segmentation pipeline is illustrated in Fig. REF .
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